With DJL, data science team can build models in different Python APIs such as Tensorflow, Pytorch, and MXNet, and engineering team can run inference on these models using DJL.

Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. DJL is designed to be easy to get started with and simple to use for Java developers.

This folder contains examples and documentation for the Deep Java Library (DJL) project. JavaDoc API Reference Note: when searching in JavaDoc, if your access is denied, please try removing the string …

DJL Serving is a high performance universal stand-alone model serving solution powered by DJL. It takes a deep learning model, several models, or workflows and makes them available through an …

Deep Java Library (DJL) is designed to be easy to get started with and simple to use. The easiest way to learn DJL is to read the beginner tutorial or our examples.

DJL is engine agnostic, so it’s capable of supporting different backends. DJL by default will select proper native library for you automatically and download those libraries from internet.

DJL core API DJL DataSet API DJL Basic ModelZoo LightGBM for DJL XGBoost for DJL MXNet Engine for DJL MXNet ModelZoo ONNX Runtime Engine for DJL PyTorch Engine for DJL PyTorch …

Inspired by Spencer Park’s IJava project, we integrated DJL with Jupyter Notebooks. For more information on the simple setup, follow the instructions in DJL Jupyter notebooks.

In this document, we will cover everything you need to build, test, and debug your code when developing DJL. Many of us use the IntelliJ IDEA IDE to develop DJL and we will sometimes mention it.

Dive into Deep Learning An interactive deep learning book with code, math, and discussions Provides Deep Java Library (DJL) implementations Adopted at 175 universities from 40 countries